Creativity is a requirement for excellence in product engineering. Despite its important role, the concept of creativity in engineering is challenging to articulate and define, both for practitioners and researchers. What makes technical creativity unique? Existing definitions often fall short of capturing the essence of creativity in technical contexts. The process of defining technical creativity is performed iteratively. In product engineering, creativity is not an abstract concept but a practical necessity, requiring motivation, imagination, expertise and experience. Therefore, two workshops with product engineers were held and the results were used to refine the definition. A shared understanding of technical creativity, that can be applied in daily engineering practice is created, enhancing both research and practical outcomes in product engineering.

Recent advances in AI offer promising opportunities for creative design, particularly through the generation of inspirational images. While prior research has explored the general benefits and limitations of text-to-image tools, there is significant potential in overcoming these constraints by investigating agile, multimodal prompting to facilitate more project-appropriate human-AI interaction. We present the development of a system designed to support both text-based and sketch-based image generation, serving as a research artefact for studying creativity support through multimodal Generative AI. The system enables dynamic dialogue interaction and visualization of the respective contributions. This paper focuses on the development of this AI system as a research artefact to enable future research through design, exploring how multimodal prompting can influence the design process.

This study presents an AI-driven method for generating preinventive structures - initial precursors to creative design concepts - using the Geneplore model as a theoretical framework. Multimodal AI is leveraged to derive preinventive structures from combinations of components of an existing product. This method is evaluated by comparing AI-generated structures of a product to those reverse identified from real repurposing solutions for the same product (IKEA hacks). The appearance of AI-generated preinventive structures in the repurposed designs suggests that this method can inspire and lead to viable design concepts. Implications extend to sustainable design, creative ideation, and the theory-driven development of design methods that support design in constrained solution spaces. Future work can refine these approaches and investigate broader applications in diverse design contexts.

Engineering design has recently undergone a paradigm shift led by generative artificial intelligence (AI). The Generative Design (GD) paradigm utilizes generative AI tools (e.g., large language models) to define the objective space and computationally exploit the design space. This is a drastic shift from the roles of human designers in the Traditional Design (TD) paradigm which consists of manual design-objective space co-evolution, and has created a research gap for Generative Design Thinking (GDT): how a designer thinks and cognitively approaches the design process during GD. To fill this gap, we propose the Paradigmatic Design Thinking Model which uniquely defines design thinking as situated within three factors (Design Cognition, Design Tools, and Design Methodology) and use it to explain design thinking in two paradigms: Traditional Design Thinking and Generative Design Thinking.

Creativity is a fundamental aspect of design that can bring us novel and useful products. However, measuring creativity in design can always be challenging as there is a lack of standardized quantification methods and the inherent limitations of mathematical modelling. Previous approaches often rely on human experts to assess design creativity. Still, humans can be subjective and biased in their evaluation procedures. Recent advancements in AI have inspired us to integrate LLMs as evaluators in engineering design. In this study, we utilize LLMs to assess the novelty and usefulness of design ideas. We developed an evaluation procedure and tested it using design samples. Experimental results demonstrate that the proposed method enhances creativity evaluation capabilities across various LLMs and improves the alignment between LLM and human expert assessments.

Advertisements play a key role in shaping perceptions of gender identity, which are influenced by biological traits and cultural beliefs. In India, practices like arranged marriages have historically defined gender roles, but younger generations are increasingly challenging these norms, especially through dating apps. This study examines how dating app advertisements address gender dynamics and societal challenges in India. By applying Barthes’ Semiotic theory, we analyzed a popular Bumble ad. The findings reveal how the ad promotes female agency, subverts gender norms, and portrays men as emotionally expressive. By blending modern technology with family values, the ad presents dating as empowering and respectful, challenging rigid societal norms. The study promotes inclusivity and shows how ads reshape gender narratives, and offers insights for creating socially responsible campaigns.

A design catalog is a repository of design problems and their solutions, enabling designers to explore and discover applicable solutions for their specific design challenges. Creating such catalogs has depended on human knowledge and implicit judgment, with no systematic approach established. This study aims to develop a systematic method to create a design catalog from patent documents. We utilize a large language model (LLM) to extract problem-solution pairs described in the documents, presenting them as general purpose-means pairs. Subsequently, we create a design catalog by classifying the problems using similarity-based clustering, enhanced by the LLM’s semantic text similarity capabilities. We demonstrate a case study of creating a design catalog for martial arts devices and generating new design concepts based on the catalog to verify the effectiveness of the proposed method.

This study investigates user engagement and its relationship with the visual aspects of design using a newly designed 3D Tic-Tac-Toe. The research examines user experience factors like cognitive engagement, fun, stress relief, etc., and to analyze their correlation with the design principles found in literature, such as Contrast, Framing, and Balance. 15 teams, comprising 2 players each, from design academic backgrounds, were provided with the game board to play. Researchers observed interactions and challenges, while subsequent surveys captured experience, aesthetics, emotional response, and design principles. The findings reveal the strong and weak correlations amongst the factors and the principles, highlights further prototype refinement. The insights integrate cognitive and emotional dimensions with core principles of design to create engaging and visually satisfying products.

This study examines the effects of linguistic elements in Vocaloid BGM on creativity, fluency and originality, aiming to design sound environments that enhance creative performance. Experiments were conducted under three BGM conditions: voiced-meaningful (VF), voiced-meaningless (VL), and non-voiced (NV). VF utilized the original Vocaloid song with lyrics, NV excluded lyrics entirely, while VL replaced lyrics with the syllable “la.” Results revealed that VF BGM could disrupt concentration and reduce creativity, while VL and NV conditions enhanced relaxation and improved originality. These effects became more pronounced as tasks progressed. A positive correlation was identified between mind-wandering tendencies and creativity, particularly with VF BGM. The findings highlight the importance of tailoring sound environments to cognitive modes and personal characteristics.

The aim of this research is to analyze the potential of Generative Artificial Intelligence (GenAI) to support the design process and overcome creative fixation in teams during the initial problem framing, ideation and concept exploration stage. Fixation is a common problem in design, and can be exacerbated during collaborative work due to diverse issues such as team dynamics or perceived hierarchy. Current research is exploring whether AI can help teams overcome this problem or on the contrary, might actually contribute to it. Through a creative ideation workshop with design students, we investigate how AI influences team dynamics as well as the creative results. We propose a conceptual model to work with AI in a team setting.

The Consensual Assessment Technique (CAT) is one of the most effective and commonly used design evaluation methods. However, it fails to capture implicit cognitive processes and has mainly been studied in a homogenous design modality. To bridge this gap, the present study investigates the impact of design ideas represented in different modalities (i.e., text-only, sketch-only, text + sketch) on design evaluations for creativity, novelty, and usefulness, and examine human gaze patterns during the evaluation process. Our findings showed that novice raters exhibit higher interrater reliability and greater convergence in visual attention when rating ideas containing sketches compared to text-only design modality, highlighting the value of visual elements in design evaluations.

Resonance, where individual creative moments resonate with each other, has been qualitatively recognized as an important phenomenon during co-creation. In a previous study, the authors conducted a concept generation pair work experiment using biosignal indicators and quantitatively grasped the difference between creative states that are simply creative and those that are resonant. This study explores whether it is possible to estimate these creative states using biosignal indicators with the Hidden Markov Model. The parameters for the Hidden Markov Model were based on multimodal biosignal indicators and subjective self-reflection reports regarding the creative states during co-creation. The results suggested that creative states can be estimated during co-creation using a Hidden Markov Model, and resonance can be understood as a shared form of self-resonance driven by concept generation.

Engineering design tasks are cognitively complex and there is a growing interest in understanding the neurocognitive processes involved in design. Consequently, researchers are increasingly using bio-physical markers such as eye tracking to study design neurocognition. However, these studies are largely correlational, and little is understood about the construct validity of eye-tracking metrics such as fixation durations and saccade frequency. Moreover, these studies rarely account for non-design factors such as neurodivergence (e.g., ADHD) on eye-tracking metrics during design. We aim to examine this research gap through a causal-comparative study with designers with and without ADHD, performing divergent and convergent design tasks. Our findings call for a deeper investigation into the construct validity of eye-tracking metrics while considering a broad range of external factors.

Generative AI, guided by inventive heuristics, can systematically and rapidly generate hundreds of ideas for engineering inventive design problems. This paper examines the reliability and effectiveness of AI-powered “idea funnelling,” a process that generates, evaluates, filters, and synthesizes raw ideas into feasible solution concepts. Key challenges include the consistency and objectivity of AI-driven evaluations, the robustness of concept generation, and the collaboration of multiple AI chatbots such as ChatGPT and Gemini. The study explores the integration of human expertise in hybrid problem-solving teams to improve feasibility, contextual relevance, and innovation quality. Through comparative experiments, it provides insights to improve the reliability of AI-driven concept creation and the performance of hybrid AI-human teams in solving complex engineering design problems.

Teams have been favored due to the diverse knowledge access. However, diversity can also have negative effects, and team outputs can be influenced by many factors, such as psychological safety. While the effects of psychological safety have been studied, its development has received less attention. Prior research in this area has focused either on specific populations or cross-sectional effects. To add to this area, this study examined the longitudinal development of psychological safety in engineering capstone students: how it evolves, and whether this can be influenced by team-related experiences. This study showed that although psychological safety did change meaningfully with time, neither time nor experience alone could capture the change. The results could shed light on the evolution of psychological safety, as well as what factors could potentially influence its development.

While performing design tasks, engineers rely heavily on their knowledge. However, the expanding knowledge space makes it impractical to perform the design tasks without external inputs. This study explores how AI can bridge the knowledge space expansion gap in design. The study introduces the AICED framework implemented as a web tool Pro-Explora, leveraging advanced multi-agent LLM technology to accelerate early-stage design tasks. Pro-Explora generates professional problem definitions, PDS documents, and unique solution concept images within five minutes, maintaining creative flow. Its effectiveness was validated in a real-life project, with outputs deemed highly relevant by experienced designers. The study highlights the AICED framework’s industry implications, addressing required knowledge. This pioneering study opens new avenues for specific LLM applications in engineering design.

This study applies Artificial Intelligence-Generated Content (AIGC) to design cultural products inspired by Sanxingdui, an ancient Chinese civilization famed for mystical bronze artifacts. Addressing the challenge of merging tradition with modernity, an AIGC framework automates cultural element extraction, generates design concepts, and optimizes aesthetics using generative models. Comparative analysis via Quality Function Deployment (QFD) shows AIGC products achieve higher user satisfaction in aesthetics, symbolism, and engagement. The research highlights the significance of AI in enhancing creativity, efficiency, and cultural preservation, despite algorithmic limitations. It provides actionable strategies for integrating AI into cultural industries, bridging heritage and technology to drive sustainable innovation.